CN117634617A - Knowledge-intensive reasoning question-answering method, device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of computers, and provides a knowledge-intensive reasoning question-answering method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining a step set required for reasoning the target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps; sequentially executing the primitives for each step in the step set, in the process, determining a target execution mode of the knowledge operation primitive corresponding to any step based on various execution modes of the knowledge operation primitive corresponding to the step and knowledge associated with various execution modes in the data manager, and executing the knowledge operation primitive corresponding to the step based on the target execution mode and knowledge associated with the target execution mode in the data manager; and determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set. The method, the device, the electronic equipment and the storage medium provided by the invention combine the symbol logic with the nerve computation to ensure the reliability of question-answering realization.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a knowledge-intensive reasoning question-answering method, apparatus, electronic device, and storage medium.
Background
The question-answering task is described in natural language as a question input, requiring the computer to give an answer to the input question. In the knowledge intensive complex reasoning scene, the execution of the question-answering task needs to clear the reasoning process for solving the problem, combines the related knowledge required by solving the problem, and applies the acquired related knowledge to the reasoning process, thereby outputting the result obtained by reasoning as an answer. Therefore, in the knowledge-intensive complex reasoning scene, the question-answering task has two characteristics of knowledge-intensive and reasoning.
Common implementations for such tasks include methods based on symbolic reasoning and methods based on neural computing. The method based on the symbol reasoning models various knowledge information as knowledge symbols to conduct question and answer, is limited by the difficulty in knowledge symbol modeling perfection, has the problem of incomplete knowledge, and is difficult to meet the requirement of intensive knowledge. The neural computing-based method is realized based on a neural network, and the neural network is represented as a black box model, so that the interpretation of knowledge questions and answers is poor.
Disclosure of Invention
The invention provides a knowledge-intensive reasoning question-answering method, a device, electronic equipment and a storage medium, which are used for solving the defects of poor completeness and poor interpretability of question-answering knowledge in a knowledge-intensive complex reasoning scene in the prior art.
The invention provides a knowledge-intensive reasoning question-answering method, which comprises the following steps:
determining a step set required for reasoning a target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
According to the knowledge-intensive reasoning question-answering method provided by the invention, under the condition that a plurality of step sets exist, primitive execution is sequentially carried out on each step in the step sets, and the method comprises the following steps:
and sequentially executing the primitives on each step in the step sets in parallel, and screening out the step set from the step sets based on the primitive execution conditions of the step sets in the primitive execution process, and stopping executing the primitives of the step set until the remaining step set in the step sets is subjected to primitive execution.
According to the knowledge-intensive reasoning question-answering method provided by the invention, the parallel primitive execution is sequentially carried out on each step in the plurality of step sets, and the method comprises the following steps:
merging the same steps in the step sets to obtain a step tree structure;
and executing primitives to each step in the plurality of step sets in parallel in the step tree structure.
According to the knowledge-intensive reasoning question-answering method provided by the invention, the determining step of the original format knowledge comprises the following steps:
Acquiring original knowledge information, wherein the original knowledge information comprises at least one of natural language text, model parameter knowledge and multi-mode picture data;
extracting elements from the original knowledge information to obtain knowledge elements corresponding to the original knowledge information, wherein the knowledge elements comprise at least one of entities, concepts, relations, attributes and qualifiers;
and taking the original knowledge information and knowledge elements corresponding to the original knowledge information as the original format knowledge.
According to the knowledge-intensive reasoning question-answering method provided by the invention, the symbol knowledge is expressed in the form of symbol elements, and the symbol elements comprise the knowledge elements;
the determining step of the knowledge related in the data manager by the execution mode comprises the following steps:
determining an operation object element of a corresponding knowledge operation primitive from any step;
and screening the knowledge which is associated with the execution mode and matched with the operation object element from the data manager, wherein the knowledge is used as the knowledge which is associated with the execution mode in the data manager.
According to the knowledge-intensive reasoning question-answering method provided by the invention, the step set required for determining the reasoning target problem comprises the following steps:
Inputting the target problem to an inference planner to obtain a step set required by the inference target problem output by the inference planner;
the reasoning planner is obtained by supervised fine tuning of a large-scale pre-trained language model.
The knowledge-intensive reasoning question-answering method provided by the invention further comprises at least one of the following steps:
receiving a data manager update instruction, and updating the data manager based on the data manager update instruction;
receiving an execution adjustment instruction, and adjusting the primitive execution process based on the execution adjustment instruction;
and receiving a step splitting instruction, and adjusting a step set required by the reasoning target problem based on the step splitting instruction.
The invention also provides a knowledge intensive reasoning question-answering device, which comprises:
the splitting unit is used for determining a step set required by reasoning the target problem, the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
An execution unit, configured to sequentially execute primitives for each step in the step set, in a primitive execution process of any step, dynamically determine, from among the various execution modes, a target execution mode of a knowledge operation primitive corresponding to any step based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and execute the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
and the output unit is used for determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the knowledge-intensive reasoning question-answering method as described in any one of the above when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a knowledge-intensive reasoning question-answering method as described in any one of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a knowledge-intensive reasoning question-answering method as described in any one of the above.
The knowledge intensive reasoning question answering method, the device, the electronic equipment and the storage medium provided by the invention are oriented to complex target problems described by natural language, step set decomposition is carried out, and corresponding knowledge operation primitives which can be executed at least in two modes of symbol and nerve computation are configured for each step in the step set, so that the answer process of the target problems does not completely depend on stored symbol logic, but combines the symbol logic with nerve computation, the imperfection of a symbol system in knowledge is made up by the nerve computation, and meanwhile, the interpretation of answers obtained by the solution is ensured by the step division and the logic computation, thereby ensuring the reliability of question answering realization.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a knowledge-intensive reasoning question-answering method provided by the invention;
FIG. 2 is a second flow chart of the knowledge-intensive reasoning question-answering method provided by the invention;
FIG. 3 is a schematic diagram of the knowledge-intensive reasoning question-answering apparatus provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Under the knowledge intensive complex reasoning scene, the question-answering task has two characteristics of knowledge intensive and reasoning. Wherein, the knowledge intensive type means that the question-answering task aims to obtain specific knowledge information and can be completed only by relying on knowledge; the inferences are that the questions in the question-answering task often need to be synthesized to answer different knowledge, and the questions can be answered instead of directly accessing and acquiring the corresponding knowledge.
At present, aiming at question-answering tasks in a knowledge-intensive complex reasoning scene, common implementation schemes comprise a method based on symbol reasoning and a method based on nerve computation.
The method based on symbol reasoning is realized through a symbol system. Symbologies are considered to have sufficient means necessary to perform general intelligent behavior that inherits the perspective of mathematical logic, i.e., the knowledge of the world can be represented by symbologies and treat intelligence as a process of symbology and reasoning following logical rules. Knowledge engineering is used as inheritor of symbol type artificial intelligence, and has the core problems of knowledge accumulation, reuse and intensive complex reasoning. The method based on symbol reasoning is to complete complex reasoning task by symbol modeling, representing knowledge as a symbol system, and converting the task described by natural language into logic expression on the symbol system to execute the logic expression. For example, entities, relationships in a knowledge graph, and triples formed therefrom, i.e., a type of knowledge symbol.
Symbol-based methods can provide an explicit reasoning process and bring about good interpretability, but they are also inevitably limited by the completeness of the symbology.
This involves two problems: (1) knowledge imperfection. It is difficult for a knowledge symbology to guarantee that all knowledge in the world is contained, and once the problem of knowledge deficiency exists, step deficiency in the reasoning process is caused, so that reasoning is difficult to continue. In practice, the knowledge itself has composable properties, and the number of knowledge faces the problem of combinatorial explosion, and there are many knowledge that cannot be represented by the existing symbology, such as common sense knowledge. Thus, knowledge imperfections will persist for a long period of time. (2) reasoning imperfections of the logical expressions. It is to be understood that all of the inference logic expressions are in a formal language. Although the formal language is complete, the complex reasoning task that natural language can express is that which is beyond complete. Thus, for question-and-answer tasks in knowledge-intensive complex reasoning scenarios, the reasoning logic expression may present an unresolved problem.
Neural computing based methods, with the development and application of continuously improved deep learning models, such as Convolutional Neural Networks (CNNs), cyclic neural networks (RNNs), fully-connected attention neural networks (convectors), and the like, and the development of Large Language Models (LLMs), have gained full acceptance in the industry. Although the method of neural calculation has better versatility (including a broad degree of knowledge, and the versatility of logic expressions), the method of neural calculation also has a certain disadvantage.
The neural computing approach faces two challenges. (1) the interpretability of the neural calculation method is very poor. Since the neural network appears as a black box optimization model, one cannot explain the behavior of the neural network model, and thus it is difficult to determine whether its output is correct. (2) The method of neural computing has a significant problem, namely, the problem of hallucinations. This means that the model may make plausible solutions to the question-answering task without actually understanding the problem, as none can absolutely guarantee the correct knowledge source.
Aiming at the problems, the embodiment of the invention provides a knowledge-intensive reasoning question-answering method. FIG. 1 is a schematic flow chart of a knowledge-intensive reasoning question-answering method provided by the invention, as shown in FIG. 1, the method comprises:
Step 110, determining a step set required for reasoning the target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode.
Specifically, the target question is a question to be answered under a question-and-answer task in a knowledge-intensive complex reasoning scene, and the target question can be input by a user in a text form or can be obtained by equipment through voice transcription after the equipment collects the voice of the user, so that the embodiment of the invention is not particularly limited.
After the target problem is obtained, reasoning can be carried out on the target problem, so that a step set required by reasoning and solving the target problem is split. Here, the step set may be obtained by training an inference planner in advance, and the inference planner may decompose the complex target problem into basic symbol units by using language understanding capability of the neural model, thereby forming an ordered step list, i.e. obtaining the step set. And, the step set generated based on the inference planning period can be one step set or a plurality of step sets, and one step set corresponds to a scheme for inferring a target question to seek an answer.
It should be understood that, herein, each step in the step set may represent an execution sequence between steps in a sequence structure, or may represent an execution sequence between steps in a tree structure or a directed acyclic graph structure, which is not specifically limited by the embodiment of the present invention.
Here, each step in the step set may be expressed by a natural language or a symbolic operation, for example, for a target problem "which is the highest peak in the a region or the B region", the corresponding step set may be "find the a region, associate it with the mountain it contains" → "do the same thing to the B region" → "calculate the union of mountains in the a region and the B region" → "pick the highest one therefrom".
And, each step in the set of steps corresponds to at least one knowledge manipulation primitive. The knowledge operation primitive is a preset operation with well defined definition, specifically a knowledge-oriented basic operation. Each knowledge manipulation primitive has a well-defined behavior and there is a description of the desired outcome. Moreover, each knowledge manipulation primitive may have multiple execution modes, for example, may be executed in a symbolic mode, or may be executed in a neural calculation mode, or may be executed in a combination of the two modes, that is, in a symbolic neural combination mode.
Further, the knowledge manipulation primitives can be divided into three broad categories, namely: 1) Direct knowledge access, retrieving elements from a data structure according to specific requirements, e.g. linking references to the entities they refer to, a process also known as entity disambiguation; 2) Knowledge processing, which operates on the output of other knowledge operations, e.g., filtering out knowledge elements that meet certain requirements; 3) Knowledge updates, provide the ability to update and refine outdated information in a data structure.
Each step in the step set can correspond to a knowledge operation primitive, so that each step is realized through the implementation of the execution mode of the knowledge operation primitive, and the reasoning and answering process aiming at the target problem is realized. The setting of knowledge manipulation primitives may serve as an interface to integrate various implementations with complementary capabilities to achieve the same goal. This enables the primitive execution process in step 120 for each step sequence in the step set to select the optimal execution mode, so as to obtain the answer corresponding to the target question as the optimal execution result. And by means of the operation of the atomization oriented knowledge, a clear meaning is given to each step in the process of reasoning the answers of the questions, and the execution result of the knowledge operation primitive corresponding to each step, namely the intermediate result of each step, is reserved. Thus, the human user can check not only the final answer, but also the output of each individual knowledge manipulation primitive, the transparency of this execution thus enhancing the interpretability.
Step 120, sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on the various execution modes of the knowledge operation primitive corresponding to any step and the knowledge associated with the various execution modes in the data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and the knowledge associated with the target execution mode in the data manager; the data manager includes symbol knowledge associated with the symbol pattern and the symbol neural combination pattern and raw format knowledge associated with the neural calculation pattern and the symbol neural combination pattern.
Specifically, after the step set is obtained, knowledge operation primitives corresponding to the steps can be sequentially executed according to the arrangement sequence of the steps in the step set. It may be understood that, for the adjacent step, after the execution of the knowledge operation primitive corresponding to the previous step is completed, the knowledge operation primitive corresponding to the next step is executed, and the result of the completion of the execution of the knowledge operation primitive corresponding to the previous step may be applied when the corresponding knowledge operation primitive of the next step is executed.
It will be appreciated that in the execution of the primitives for each step, knowledge associated with the knowledge operation primitive for each step needs to be applied when executing the manner in which the knowledge operation primitive is executed. Thus, the data manager may be preset and knowledge for problem reasoning stored in the data manager.
Here, the knowledge in the data manager can be divided into two categories, namely symbol knowledge and raw format knowledge. Where symbolic knowledge is the knowledge symbolized in a knowledge base of a traditional type, e.g. triples, quaternions, and also e.g. knowledge maps. Raw format knowledge is knowledge that is scattered in the raw format, without symbolization, such as plain text, model parameter knowledge, images, and tables.
It can be appreciated that different types of knowledge can be associated with different execution manners of the knowledge operation primitives, further, the symbol manner is a symbol-knowledge-oriented knowledge operation primitive execution manner, the neural calculation manner is an original-format-knowledge-oriented knowledge operation primitive execution method, and the symbol neural combination manner is a symbol-knowledge-and original-format-knowledge-oriented knowledge operation primitive execution method.
By covering two types of knowledge, namely symbol knowledge and original format knowledge in the data manager, the imperfection of the symbol knowledge can be made up by applying the original format knowledge, and the knowledge coverage can be enlarged. And compensates for the lack of interpretability of neural calculations based on raw format knowledge by application of symbolic knowledge,
taking the primitive execution process of any step in the step set as an example, the fact that a plurality of execution modes exist in the knowledge operation primitive corresponding to the step is considered, and different types of knowledge are associated in the data manager by different execution modes. At least one preferred execution mode from the various execution modes can be selected as a target execution mode by combining the calculation cost, precision and other factors of the various execution modes and the reliability, integrity and other factors of knowledge related to the various execution modes in the data manager, and the knowledge operation primitive corresponding to the step is executed based on the target execution mode and the knowledge related to the target execution mode, so that the result of executing the primitive by the step is obtained. It will be appreciated that the determination of the target execution manner is dynamic here, i.e. the knowledge operation primitives for the different steps may determine the target execution manner separately, or the same knowledge operation primitives in the different synchronization sets may determine the target execution manner separately. In addition, in the case that a plurality of step sets exist, the plurality of step sets can execute knowledge operation primitives in parallel, the step sets are dynamically screened out in the process to realize pruning aiming at the target problem solving process, and the determination of the target execution mode of the knowledge operation primitives in the pruning process is dynamically executed.
For example, when the execution mode of the knowledge operation primitive corresponding to the "find the highest peak of the area a" is a symbolic mode, the related information of the highest peak of the area a needs to be searched from the knowledge base in the data manager, and when the execution mode is a neural calculation mode, the semantic understanding needs to be performed on the original format knowledge. In various implementations, the knowledge obtained from the text "peak C is the highest mountain in area A and around the world" is an accurate and possibly fastest way to answer the target question, and the target implementation is chosen to obtain such an optimal solution.
That is, in the primitive execution process for any step, the target execution mode is selected from multiple execution modes corresponding to the knowledge operation primitive, and the knowledge operation primitive corresponding to the step is executed based on the target execution mode, so that the obtained primitive execution result is ensured to be the optimal solution of the primitive execution of the step.
And 130, determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
Specifically, after the primitive execution of each step in the step set is sequentially completed, an answer corresponding to the target question may be determined based on the result of executing the primitive of each step in the step set. For example, the result of primitive execution of the last step in the step set can be used as an answer corresponding to the target question; for example, the result of primitive execution performed by each step in the step set may be combined to serve as an answer corresponding to the target question, which is not particularly limited in the embodiment of the present invention.
The method provided by the embodiment of the invention is oriented to complex target problems described by natural language, step set decomposition is carried out, and corresponding knowledge operation primitives which can be executed at least through two modes of symbol and nerve calculation are configured for each step in the step set, so that the solving process of the target problems does not completely depend on stored symbol logic, the symbol logic is combined with nerve calculation, the imperfection of a symbol system in knowledge is compensated through nerve calculation, and meanwhile, the interpretability of answers obtained by solving is ensured through a step division solving mode and logic calculation, thereby ensuring the reliability of question-answer realization.
In addition, in the method, symbol knowledge and original format knowledge are covered in the data manager, so that completeness of knowledge is ensured.
Based on the above embodiment, in step 110, the step set required for reasoning about the target problem may be one step set or may be a plurality of step sets.
It will be appreciated that the resolution of the target problem may be achieved by different mental chain reasoning processes, whereby there may be a plurality of different sets of steps, i.e. corresponding to different mental chain reasoning processes, required to reason the target problem.
For example, for the objective problem "which peak is the highest in the a region or the B region", the idea of solving the problem may be to find the highest peaks in the a region and the B region, respectively, and then compare their heights, or may be to collect the heights of all mountains in the a region and the B region and then find the highest one therefrom. Thus, the target problem "which of the highest peaks in region a or region B" may exist as a set of at least two steps.
On this basis, in step 120, the primitive execution is sequentially performed for each step in the step set, including:
and sequentially executing the primitives on each step in the step sets in parallel, and screening out the step set from the step sets based on the primitive execution conditions of the step sets in the primitive execution process, and stopping executing the primitives of the step set until the remaining step set in the step sets is subjected to primitive execution.
In particular, in the case where there are a plurality of step sets, primitive execution may be performed in parallel for each step order in the plurality of step sets. That is, each step set may perform primitive execution on the sequence of steps in the step set based on the execution manner of step 120 in the above embodiment.
In the execution process, primitive execution conditions based on each step set are updated in real time, and the primitive execution conditions can comprise efficiency, calculation cost, precision, recall rate and the like of primitive execution. Meanwhile, the method can evaluate the superiority and inferiority of each step set on the reasoning of the target problem based on the primitive execution condition of each step set, so that the step set with poor execution condition is eliminated in real time, namely, the step set with poor execution condition is selected from each step set to be used as an elimination step set, and primitive execution of the elimination step set is stopped, so that the continuous operation of the elimination step set with poor execution condition in the part is avoided, and extra calculation burden is brought to a system.
The above scheme of performing step set elimination in real time during primitive execution may be continued until one step set remains for primitive execution. It can be understood that the last remaining step set, i.e. the step set with the best execution condition in all the step sets, is also the step set with the best solution for finding the objective problem reasoning most easily.
Based on any of the foregoing embodiments, in step 120, the executing the primitive sequentially on each step in the plurality of step sets in parallel includes:
Merging the same steps in the step sets to obtain a step tree structure;
and executing primitives to each step in the plurality of step sets in parallel in the step tree structure.
Specifically, the same steps may exist in different step sets, so as to avoid repeatedly executing the same steps in different step sets in the process of executing primitives on the steps in multiple step sets in parallel, the same steps may be combined before the step sets, so that multiple step sets may be displayed in the implementation of the step tree structure.
In the step tree structure, each step corresponds to one node in the tree structure, the same step is the same node in the tree structure, different step sets are embodied in different execution paths in the tree structure in the step tree structure, and the different execution paths can pass through the same node.
Thus, after the step tree structure is obtained, each step in the plurality of step sets can be executed in parallel in the step tree structure and the primitive execution is performed sequentially, thereby avoiding the repeated execution of the same step in different step sets.
Based on any of the above embodiments, the determining of the raw format knowledge includes:
Acquiring original knowledge information, wherein the original knowledge information comprises at least one of natural language text, model parameter knowledge and multi-mode picture data;
extracting elements from the original knowledge information to obtain knowledge elements corresponding to the original knowledge information, wherein the knowledge elements comprise at least one of entities, concepts, relations, attributes and qualifiers;
and taking the original knowledge information and knowledge elements corresponding to the original knowledge information as the original format knowledge.
Here, the original knowledge information is knowledge information in an original format, which can be directly obtained, such as natural language text, model parameter knowledge, and multi-modal picture data. It is understood that the raw knowledge information can be directly used as raw format knowledge, and applied to the execution of knowledge operation primitives in a neural computing manner.
On this basis, the problem that the neural calculation often has illusion due to the lack of an absolute correct knowledge source is considered, so that the accuracy of the answer obtained based on the neural calculation mode is poor. In order to solve the problem, after the original knowledge information is obtained, the embodiment of the invention performs element extraction on the original knowledge information, so that knowledge elements corresponding to the obtained original knowledge information are obtained.
Here, the element extraction for the original knowledge information may include at least one of entity extraction, concept extraction, relationship extraction, attribute extraction, and qualifier extraction. Wherein the Entities (Entities) are the only objects in the real world; concepts (Concepts) are collections of entities with similar attributes; relationships (relationships) describe connections between entities and concepts; attributes (Attributes) are associated with entities to describe them from some aspects; qualifiers (Qualifiers) are constraints on other knowledge items to specify under which conditions they are true.
After the elements are obtained through extraction, the original knowledge information and knowledge elements corresponding to the original knowledge information can be used as original format knowledge. It will be appreciated that in the raw format knowledge thus obtained, knowledge elements may impose constraints on the raw knowledge information, thereby improving the accuracy of access to the raw knowledge information in the data manager, and thereby reducing the illusion of answers given by neural computing.
For example, in the related art, original knowledge information in the form of natural language text only allows fuzzy similarity matching with query words, but in the embodiment of the invention, for the original knowledge information carrying knowledge elements, concept constraint can be performed while entity retrieval is performed, so that accuracy of knowledge access is improved.
Based on any of the above embodiments, the symbolic knowledge is represented in the form of symbolic elements, the symbolic elements comprising the knowledge elements.
Thus, two types of knowledge in the data manager, namely symbol knowledge and original format knowledge, take corresponding knowledge elements as representation forms.
That is, in the data manager, both the symbolic knowledge and the raw format knowledge are represented in a unified form of knowledge elements and knowledge information is provided so that knowledge operation primitives can perform unified knowledge operations.
Accordingly, in step 120, the determining the knowledge associated in the data manager by the implementation manner includes:
determining an operation object element of a corresponding knowledge operation primitive from any step;
and screening the knowledge which is associated with the execution mode and matched with the operation object element from the data manager, wherein the knowledge is used as the knowledge which is associated with the execution mode in the data manager.
Specifically, in the process of executing the primitives for each step sequence in step 120, for any step, an operation object element corresponding to the knowledge operation primitive may be determined from the step, and it may be understood that the operation object element herein, that is, an element corresponding to a knowledge element carried by various kinds of knowledge in the data manager, that is, the operation object element may also include at least one of an entity, a concept, a relationship, an attribute, and a qualifier. For example, when the knowledge operation primitive is direct knowledge access, the operation object element is a knowledge element carried by the knowledge to be accessed.
After the knowledge operation primitive and the operation object element of the step are determined, the knowledge of which the knowledge type is associated with the execution mode of the knowledge operation primitive and the carried knowledge element is matched with the operation object element of the step can be screened from the data manager as the knowledge associated in the data manager by the execution mode, so that the knowledge-oriented operation is realized.
Based on any of the above embodiments, step 110 includes:
inputting the target problem to an inference planner to obtain a step set required by the inference target problem output by the inference planner;
the reasoning planner is obtained by supervised fine tuning of a large-scale pre-trained language model.
In particular, the inference planner is used to decompose the target problem embodying knowledge-intensive complex inference tasks into a combination of knowledge operation primitives. As an interface, the inference planner understands the needs of humans and represents the needs embodied by the target problem as an inference process, i.e. a collection of steps.
Since the primary task of the inference planner is to refine ambiguous human intent to have a combination of well-defined knowledge manipulation primitives, in embodiments of the present invention, a pre-trained large-scale pre-training language model is applied to construct the inference planner, e.g., the large-scale pre-training language model can be subjected to supervised fine tuning to obtain the inference planner. Because the large-scale pre-training language model has excellent language understanding and generating capability, the potential of planning according to natural language prompts is displayed, so that the generated reasoning planner can realize step splitting of target problems and also has the capability of extracting higher-order intentions from natural language descriptions, thereby realizing implicit reasoning tasks aiming at the target problems.
As an example, the embodiment of the invention also provides a method applied to the inference planner to output the step set required by the inference target problem, and in particular, the inference planner can construct a hierarchical problem decomposition tree to represent the complex target problem. Here, the root node of the hierarchical problem decomposition tree, that is, the target problem to be inferred, and the non-root node represents the child problem obtained by decomposing for its parent node, and the hierarchical problem decomposition tree can decompose the problem layer by layer until it is decomposed to the leaf node. The leaf node here may be referred to as an atomic problem, as well as a simple problem in which decomposition cannot continue. By constructing a hierarchical problem decomposition tree, complex target problems can be decomposed into atomic problems, and on the basis, corresponding knowledge operation primitives are matched for the atomic problems, so that a step set required for reasoning the target problems can be obtained.
Based on any of the above embodiments, the method further comprises at least one of:
receiving a data manager update instruction, and updating the data manager based on the data manager update instruction;
receiving an execution adjustment instruction, and adjusting the primitive execution process based on the execution adjustment instruction;
And receiving a step splitting instruction, and adjusting a step set required by the reasoning target problem based on the step splitting instruction.
Specifically, the question-answering method provided by the embodiment of the invention further comprises a human-in-loop solution mechanism, and the human is operated to improve the understanding and performance of all parts applied by the question-answering method to the world through feedback.
Wherein the data manager update instruction is for indicating a knowledge update of the data manager. By receiving the data manager update instruction, a knowledge update loop may be implemented. That is, a human user can update knowledge stored in the data manager based on the data manager update instruction, and can specifically check and correct the knowledge, and annotate the newly involved knowledge.
The execution adjustment instruction is used to adjust the procedure performed by the primitive in the problem inference. By receiving the execution adjustment instruction, a program repair loop can be realized. In the program repair loop, a human user can find the wrong program given by the inference planner and help to select the optimal inference path when the primitives are executed, for example, selecting the optimal step set or the optimal knowledge operation primitives, or selecting the optimal execution mode. In the process, people can help the question and answer system to immediately solve the current complex reasoning task, and training data can be accumulated for further training a better reasoning planner and a reasoning executor. It will be appreciated that the problem solver herein is used to perform step 120 in the above embodiments.
The step splitting instruction is used for adjusting the step set. By receiving the step splitting instruction, a manual auxiliary problem solving loop can be realized. Under the manual auxiliary problem solving loop, human beings are used for instantly decomposing a complex problem reasoning task into a simpler reasoning task in a conversational manner by splitting instructions through steps. The simple reasoning task is solved through the coordination of the question-answering system and the human user, and the output of the simple reasoning task is assembled into the final answer.
Based on any of the above embodiments, fig. 2 is a second schematic flow chart of the knowledge-intensive reasoning question-answering method provided by the present invention, and as shown in fig. 2, the implementation steps of the knowledge-intensive reasoning question-answering method are as follows:
the inference executor 210, the inference planner 220, the data manager 230, and the knowledge manipulator 240 need to be built in advance before the question-answering.
Wherein the data manager 230 is configured to aggregate knowledge of multiple sources, where the knowledge of multiple sources includes knowledge of symbols of knowledge base sources, and knowledge of original formats dispersed in the original formats. In the data manager 230, both the symbolic knowledge and the raw format knowledge are tagged with knowledge elements, and the knowledge elements are unified.
The knowledge manipulator 240 is configured to provide well-defined knowledge manipulation primitives on the data manager 230, where the knowledge manipulation primitives are directed to knowledge in the data manager 230, and the knowledge manipulation primitives may be divided into three general classes, namely: 1) Direct knowledge access, retrieving elements from a data structure according to specific requirements; 2) Knowledge processing, namely operating the output of other knowledge operations; 3) Knowledge updates, provide the ability to update and refine outdated information in a data structure.
The inference planner 220 is used to decompose the target problem embodying knowledge-intensive complex inference tasks into combinations of knowledge operation primitives. The inference planner 220 may be a supervised fine tuning based on a large scale pre-trained language model.
The inference executor 210 is configured to perform inference according to the set of steps given by the inference planner 220 and output a final answer.
In determining the existence of the inference executor 210, the inference planner 220, the data manager 230, and the knowledge manipulator 240 described above, the problem method may be performed:
first, a target problem may be acquired.
Second, the objective questions are input into the inference planner 220, and the inference process of the objective questions is decomposed into combinations of knowledge operation primitives by the inference planner 220, i.e., a set of steps required for inferring the objective questions.
The inference planner 220 then sends the split step set to the inference executor 210, and the inference executor 210 may call the knowledge operation primitives defined in the knowledge operator 240 according to the order in the step set, access the data manager 230 to perform inference, and select an optimal inference process in the process to seek to obtain an optimal solution, i.e. obtain an answer to the target question.
Based on any of the above embodiments, fig. 3 is a schematic structural diagram of a knowledge-intensive reasoning question-answering device provided by the present invention, as shown in fig. 3, the device includes:
a splitting unit 310, configured to determine a step set required for reasoning the target problem, where the step set includes knowledge operation primitives corresponding to each step, where the knowledge operation primitives are knowledge-oriented basic operations, and an execution mode of the knowledge operation primitives includes at least two of a symbol mode, a nerve computation mode, and a symbol nerve combination mode;
an execution unit 320, configured to sequentially execute primitives for each step in the step set, in a primitive execution process of any step, dynamically determine, from among the various execution modes, a target execution mode of a knowledge operation primitive corresponding to any step based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and execute the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And an output unit 330, configured to determine an answer corresponding to the target question based on a result of primitive execution performed by the steps in the step set.
The device provided by the embodiment of the invention is oriented to complex target problems described by natural language, carries out step set decomposition, configures corresponding knowledge operation primitives which can be executed at least in two modes of symbol and nerve calculation for each step in the step set, so that the solving process of the target problems does not completely depend on stored symbol logic, combines the symbol logic with nerve calculation, compensates the imperfection of a symbol system in knowledge through the nerve calculation, and ensures the interpretability of answers obtained by solving through the step division and the logic calculation, thereby ensuring the reliability of question-answer realization.
Based on any of the above embodiments, in case there are multiple step sets, the execution unit 320 is configured to:
and sequentially executing the primitives on each step in the step sets in parallel, and screening out the step set from the step sets based on the primitive execution conditions of the step sets in the primitive execution process, and stopping executing the primitives of the step set until the remaining step set in the step sets is subjected to primitive execution.
Based on any of the above embodiments, in the case where there are multiple step sets, the execution unit 320 is specifically configured to:
merging the same steps in the step sets to obtain a step tree structure;
and executing primitives to each step in the plurality of step sets in parallel in the step tree structure.
Based on any of the above embodiments, the apparatus further includes a knowledge management unit configured to:
acquiring original knowledge information, wherein the original knowledge information comprises at least one of natural language text, model parameter knowledge and multi-mode picture data;
extracting elements from the original knowledge information to obtain knowledge elements corresponding to the original knowledge information, wherein the knowledge elements comprise at least one of entities, concepts, relations, attributes and qualifiers;
and taking the original knowledge information and knowledge elements corresponding to the original knowledge information as the original format knowledge.
Based on any of the above embodiments, the symbolic knowledge is represented in the form of symbolic elements, the symbolic elements including the knowledge elements;
the determining step of the knowledge related in the data manager by the execution mode comprises the following steps:
determining an operation object element of a corresponding knowledge operation primitive from any step;
And screening the knowledge which is associated with the execution mode and matched with the operation object element from the data manager, wherein the knowledge is used as the knowledge which is associated with the execution mode in the data manager.
Based on any of the above embodiments, the splitting unit 310 is configured to:
inputting the target problem to an inference planner to obtain a step set required by the inference target problem output by the inference planner;
the reasoning planner is obtained by supervised fine tuning of a large-scale pre-trained language model.
Based on any of the above embodiments, the apparatus further comprises a person-in-loop unit for:
receiving a data manager update instruction, and updating the data manager based on the data manager update instruction;
receiving an execution adjustment instruction, and adjusting the primitive execution process based on the execution adjustment instruction;
and receiving a step splitting instruction, and adjusting a step set required by the reasoning target problem based on the step splitting instruction.
Fig. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a knowledge-intensive reasoning question-answering method that includes:
Determining a step set required for reasoning a target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the knowledge-intensive inference question-answering method provided by the above methods, the method comprising:
Determining a step set required for reasoning a target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the knowledge-intensive reasoning questioning method provided by the methods described above, the method comprising:
determining a step set required for reasoning a target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A knowledge-intensive reasoning question-answering method, comprising:
determining a step set required for reasoning a target problem, wherein the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
sequentially executing the primitives for each step in the step set, in the primitive execution process of any step, dynamically determining the target execution mode of the knowledge operation primitive corresponding to any step from the various execution modes based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and executing the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
And determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
2. The knowledge-intensive inference question-answering method according to claim 1, wherein in a case where there are a plurality of step sets, the sequentially performing primitive execution for each step in the step set includes:
and sequentially executing the primitives on each step in the step sets in parallel, and screening out the step set from the step sets based on the primitive execution conditions of the step sets in the primitive execution process, and stopping executing the primitives of the step set until the remaining step set in the step sets is subjected to primitive execution.
3. The knowledge-intensive inference question-answering method according to claim 2, wherein the parallel primitive execution of each step in the plurality of step sets sequentially includes:
merging the same steps in the step sets to obtain a step tree structure;
and executing primitives to each step in the plurality of step sets in parallel in the step tree structure.
4. The knowledge-intensive reasoning questioning method of claim 1, wherein the determining of the raw format knowledge comprises:
acquiring original knowledge information, wherein the original knowledge information comprises at least one of natural language text, model parameter knowledge and multi-mode picture data;
extracting elements from the original knowledge information to obtain knowledge elements corresponding to the original knowledge information, wherein the knowledge elements comprise at least one of entities, concepts, relations, attributes and qualifiers;
and taking the original knowledge information and knowledge elements corresponding to the original knowledge information as the original format knowledge.
5. The knowledge-intensive reasoning questioning method of claim 4, wherein the symbolic knowledge is represented in the form of symbolic elements, the symbolic elements comprising the knowledge elements;
the determining step of the knowledge related in the data manager by the execution mode comprises the following steps:
determining an operation object element of a corresponding knowledge operation primitive from any step;
and screening the knowledge which is associated with the execution mode and matched with the operation object element from the data manager, wherein the knowledge is used as the knowledge which is associated with the execution mode in the data manager.
6. The knowledge-intensive inference question-answering method according to any one of claims 1 to 5, wherein the set of steps required to determine an inference target question includes:
inputting the target problem to an inference planner to obtain a step set required by the inference target problem output by the inference planner;
the reasoning planner is obtained by supervised fine tuning of a large-scale pre-trained language model.
7. The knowledge-intensive inference question-answering method according to any one of claims 1 to 5, further comprising at least one of:
receiving a data manager update instruction, and updating the data manager based on the data manager update instruction;
receiving an execution adjustment instruction, and adjusting the primitive execution process based on the execution adjustment instruction;
and receiving a step splitting instruction, and adjusting a step set required by the reasoning target problem based on the step splitting instruction.
8. A knowledge-intensive reasoning questioning and answering apparatus, comprising:
the splitting unit is used for determining a step set required by reasoning the target problem, the step set comprises knowledge operation primitives corresponding to the steps, the knowledge operation primitives are knowledge-oriented basic operations, and the execution modes of the knowledge operation primitives comprise at least two of a symbol mode, a nerve calculation mode and a symbol nerve combination mode;
An execution unit, configured to sequentially execute primitives for each step in the step set, in a primitive execution process of any step, dynamically determine, from among the various execution modes, a target execution mode of a knowledge operation primitive corresponding to any step based on various execution modes of the knowledge operation primitive corresponding to any step and knowledge associated with the various execution modes in a data manager, and execute the knowledge operation primitive corresponding to any step based on the target execution mode and knowledge associated with the target execution mode in the data manager; the data manager comprises symbol knowledge and original format knowledge, wherein the symbol knowledge is associated with the symbol mode and the symbol nerve combination mode, and the original format knowledge is associated with the nerve calculation mode and the symbol nerve combination mode;
and the output unit is used for determining an answer corresponding to the target question based on the result of primitive execution of the steps in the step set.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the knowledge-intensive inference question-answering method according to any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the knowledge-intensive inference question-answering method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118070893A (en) * | 2024-04-24 | 2024-05-24 | 北京百川智能科技有限公司 | Information feedback method, device and computer program product based on large language model |
| CN118070893B (en) * | 2024-04-24 | 2024-07-23 | 北京百川智能科技有限公司 | Information feedback method, device and computer program product based on large language model |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20220019461A (en) * | 2020-08-10 | 2022-02-17 | 경기대학교 산학협력단 | System for question answering knowledge graphs using graph neural network |
| CN114817512A (en) * | 2022-06-28 | 2022-07-29 | 清华大学 | Question-answer reasoning method and device |
| CN115617954A (en) * | 2022-11-17 | 2023-01-17 | 清华大学 | Question answering method and device, electronic equipment and storage medium |
| CN115829033A (en) * | 2023-02-02 | 2023-03-21 | 中国科学技术大学 | Mathematics application question knowledge construction and solution method, system, equipment and storage medium |
| CN116561273A (en) * | 2023-04-20 | 2023-08-08 | 清华大学 | Text problem solving method and device, electronic equipment and storage medium |
| CN117076688A (en) * | 2023-08-18 | 2023-11-17 | 中国工商银行股份有限公司 | Knowledge question-answering method and device based on domain knowledge graph and electronic equipment |
-
2024
- 2024-01-25 CN CN202410102332.4A patent/CN117634617B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20220019461A (en) * | 2020-08-10 | 2022-02-17 | 경기대학교 산학협력단 | System for question answering knowledge graphs using graph neural network |
| CN114817512A (en) * | 2022-06-28 | 2022-07-29 | 清华大学 | Question-answer reasoning method and device |
| CN115617954A (en) * | 2022-11-17 | 2023-01-17 | 清华大学 | Question answering method and device, electronic equipment and storage medium |
| CN115829033A (en) * | 2023-02-02 | 2023-03-21 | 中国科学技术大学 | Mathematics application question knowledge construction and solution method, system, equipment and storage medium |
| CN116561273A (en) * | 2023-04-20 | 2023-08-08 | 清华大学 | Text problem solving method and device, electronic equipment and storage medium |
| CN117076688A (en) * | 2023-08-18 | 2023-11-17 | 中国工商银行股份有限公司 | Knowledge question-answering method and device based on domain knowledge graph and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 曹书林等: "知识库问答研究进展与展望", 计算机学报, vol. 46, no. 3, 13 March 2023 (2023-03-13), pages 512 - 538 * |
| 高明霞;刘椿年;: "基于模糊描述逻辑的PNL网络问答***", 计算机工程, no. 21, 5 November 2006 (2006-11-05), pages 52 - 54 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118070893A (en) * | 2024-04-24 | 2024-05-24 | 北京百川智能科技有限公司 | Information feedback method, device and computer program product based on large language model |
| CN118070893B (en) * | 2024-04-24 | 2024-07-23 | 北京百川智能科技有限公司 | Information feedback method, device and computer program product based on large language model |
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